System identification via sweep excitations suffers from transients in the case of high frequency rates and low system damping. This contribution presents a novel method for a time domain generation of sweep signals to accurately estimate the frequency response function of a linear system within a desired sweep time. The approach is based on a characteristic value for determining the harmony of a signal, which was previously presented by the authors. It has been empirically found that this characteristic value is directly related to the squared derivative of the period duration of a sweep signal. Therefore, it can be used to shape a desired frequency characteristic in a way that suppresses transient effects of the system response compared to basic sweep approaches. The method is optimized to identify a single degree of freedom oscillator via particle swarm optimization. It is shown that the identification via an envelope of the system response can be enhanced by approximately 70 % compared to basic sweep signals for a weak damped oscillator. Therefore, the approach mitigates the trade-off between time requirements and accuracy of system identification via sweep excitations, if a rough estimate of the resonant frequency and the damping ratio is available.

Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD) perform in the expected manner? Will they actually make road traffic safer, or will they potentially introduce new critical situations or road accidents? It is almost impossible to address these questions solely through real-world tests. A promising tool to provide appropriate answers in a time-and cost-efficient way without exposing subjects to risk are virtual assessment methods. Reliable safety assessments are only possible, if the traffic simulations provide realistic traffic, including critical situations and road accidents. This paper provides a review of how human error contributes to critical situations and accidents in road traffic. The focus is on the causes and mechanisms of human error, which driver behavior models must address in order to simulate realistic traffic. For this purpose, Rasmussen’s error taxonomies are applied to the traffic context and extended with further research. The paper shows the causes of those human errors and that the underlying mechanisms thereof should be taken into account in order to obtain more transparent and realistic driver behavior models. It is shown, which concepts for modelling realistic traffic exist and how virtual safety assessment could benefit from this development. In addition, the driver behavior model DReaM (Driver Reaction Model) is presented to address the issues resulting from existing cognitive driver models.

The purpose of this paper is to analyze naturalistic driving data and crash data in the United States of America concerning the multiple risk-inducing factors which exist in real traffic. The derived method allows to identify neutral characteristics occurring in many situations and extract risk-inducing attributes from real data by conducting the Successive Odds Ratio Analysis (SORA). The SORA algorithm uses two different types of data, e.g., baseline and crash data, calculates the criticality of each attribute, and evaluates combinations whereby the total criticality is affected positively or negatively. This paper focuses on the exemplary environment-related variables which are provided by the considered databases. Based on identified risk-inducing attributes, their associated characteristics will be investigated by using three measures, i.e., Support, Confidence, and Lift. The method has the potential to generate a scenario catalog consisting of critical test cases for the development of advanced driver assistance systems.

A target-oriented conceptual process for the layout of vehicle-control systems leads to efficient product development. Enabling a target-oriented layout, a functional system description is necessary to define the system requirements, e.g., a maximum system torque or a defined minimum time constant. This paper introduces a novel approach to a systematic development of functional models for the quantification of the vehicle-control system requirements. The potential of the method is verified by applying it to the active roll stabilisation system (ARS).

In robotic manipulation the interaction between robot and environment is an essential part of the task. In order to deal with impacts, friction and constrained motion, a model-based optimization approach is suggested which relies on a detailed dynamic model of the manipulator itself, the process dynamics of the task and the interactions between those two. These models are used to define an optimization problem, which is then solved using numerical programming methods. It is illustrated with an assembly task, namely mating a pair of snap fits. Utilizing a pre-optimized dynamic response behavior of the robot we plan trajectory and design parameters of the mating parts, which influence the process dynamics and thus the external excitation of the manipulator dynamics. The expected advantage in industrial applications is a relatively easy implementation, because performance can be improved by simply adjusting 'external' parameters as path and geometry. Particularly, no modifications of the control architecture are required. Application of the proposed approach to a snap joint under practical constraints can reduce the cycle time by more than 45%.

The paper contributes to the problem of robotic manipulation where the interaction between robot and environment is an essential part of the task. In order to deal with impacts, oscillations and constrained motion, a model-based optimization approach is suggested, which relies on a detailed dynamic model of the manipulator incorporating finite gear stiffnesses and damping. These models are used to define an optimization problem, which is then solved using numerical programming methods. The intention of the proposed method is to provide a tool in the planning stage of an assembly task for an efficient manipulator optimization with respect to increased productivity and reliability. It is illustrated with an assembly task, namely inserting a rigid peg into a hole with a PUMA 562 manipulator. The robot's position and its control coefficients, which have a large influence on its dynamic response behavior, are adjusted according to the specific needs of the manipulation task to be carried out. The expected advantage in industrial applications is a comparatively easy implementation, because performance can be improved by simply adjusting "external" parameters such as mating position and coefficients of the standard joint controller. Particularly, no modifications of the control architecture and no additional hardware are required.

A method for the planning of robotic assembly by numerical optimization of position and joint controller coefficients is proposed in this paper. Starting from a detailed modelling of the robot's dynamics incorporating joint elasticities and damping, scalar optimization criteria are formulated, the minimization of which yields an improved performance during assembly. Together with constraints ensuring practical applicability a nonlinear vector optimization problem is stated for a peg-inhole insertion task and solved using an SQP-algorithm. Discussion of the Pareto-optimal region for this example shows that the dynamic performance of the robot can be tuned within a wide range according to the specific properties of the mating process by adjusting the robot's position and its joint controller coefficients. Although finding an absolute minimum of the cost functions requires rather strong restrictions, an automatic optimization routine can be expected to work more effectively than an intera...